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MCP with RAG Demo

@thinklytics

リポジトリ

基本情報

カテゴリ

メモリとナレッジ

ライセンス

MIT license

トランスポート

stdio

作者

thinklytics

MCP with RAG Demo について

This demonstration project shows how to implement a Model Context Protocol (MCP) server with Retrieval-Augmented Generation (RAG) capabilities. The demo allows AI models to interact with a knowledge base, search for information, and add new documents.

MCP with RAG Demo の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-demo-thinklytics": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        "mcp-env"
      ]
    }
  }
}

MCP with RAG Demo のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is MCP with RAG Demo?

MCP with RAG Demo is an example implementation of a Model Context Protocol (MCP) server integrated with Retrieval-Augmented Generation (RAG) capabilities. It allows AI models to interact with a knowledge base, search for information, and add new documents. The project is intended for developers learning how to build MCP servers with RAG.

How to use MCP with RAG Demo?

Install dependencies with pip install -r requirements.txt, then start the MCP server in either SSE mode (python server.py --sse) or stdio mode (python server.py --stdio). Run the client example with python client_example.py --sse http://localhost:8000 for SSE or python client_example.py --stdio --command python --args "server.py --stdio" for stdio. An OpenAI integration example is also available via python openai_example.py after setting the OPENAI_API_KEY environment variable.

Key features of MCP with RAG Demo

  • MCP server with tool and resource support
  • RAG implementation with in-memory storage fallback
  • Client example for interacting with the MCP server
  • Support for both SSE (HTTP) and stdio communication modes
  • Simple prompt templates for greetings

Use cases of MCP with RAG Demo

  • Demonstrating how to build an MCP server with RAG capabilities
  • Adding documents to a knowledge base and searching them with AI
  • Testing MCP communication over SSE or stdio
  • Integrating OpenAI’s API with an MCP server

よくある質問

1

What tools are available in MCP with RAG Demo?

The demo provides five tools: `echo`, `add`, `add_document`, `rag_search`, and `list_documents`. Each tool has a specific purpose, such as echoing messages or adding documents to the knowledge base.

2

What communication modes does the server support?

The server supports SSE (HTTP) mode using Server-Sent Events and stdio mode for standard input/output communication. Default SSE host is `0.0.0.0:8000`, customizable with `--host` and `--port`.

3

What are the runtime requirements?

Python 3.8+ and pip are required. Dependencies are listed in `requirements.txt` and include the `mcp-sdk` package. An OpenAI API key is needed for the OpenAI example.

4

Where does the knowledge base data live?

The knowledge base uses in-memory storage (no persistent database). Documents are held only while the server is running. Sample data is available from the `sample://data` resource.

5

How do I troubleshoot connection issues?

Ensure the server is running in another terminal, check the URL (default `http://localhost:8000`), and verify the server is listening on the expected interface. Use `--debug` for verbose output. The client automatically appends `/sse` if missing.

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